Overview

View Color-Coded Results

Init

Code
rm(list = ls(all = TRUE))
options(tidyverse.quiet = TRUE)
library(plyr)
library(tidyverse)
library(RColorBrewer)

Data

Code
ifd0 <- "data/i0001-src"
ofd0 <- "data/i0002-out"
dir.create(ofd0, showWarnings=FALSE, recursive=TRUE)
ifn0 <- "jgnb-df9v.csv"
suppressWarnings(rm(list=ls(pattern="^df")))
df0 <- readr::read_csv(
  file = file.path(ifd0, ifn0),
  show_col_types = FALSE,
  col_types =  list(
     Sub = col_factor(),
     Ses = col_factor(),
     Run = col_factor(),
     Chrono = col_factor(),
     Condit = col_factor(),
     Gender = col_factor(),
     Education = col_factor(),
     TP = col_integer(),
     FP = col_integer(),
     TN = col_integer(),
     FN = col_integer(),
     NiiAcqTime = col_character(), ## col_time(format = ""),
     NiiAcqTIME = col_character(),
     LogFileTime = col_character(),
     .default = col_guess()),
  ) %>%
  dplyr::filter(Run %in% c("01", "02", "03")) %>%
  identity()

df0 %>% dim()
[1] 900  74

Factor Levels

Code
df2 <- df0 %>%
  mutate(
    Ses = factor(Ses, levels=c("Morning", "Evening")),
    Run = factor(Run, levels=c("02", "01", "03")),
    Chrono = factor(Chrono, levels = c("Morning", "Evening")),
    Condit = factor(Condit, levels = c("Congruent", "Incongruent")),
  ) %>%
  mutate(
    NumBack = as.integer(NumBack),
    NumBack = paste0("n", sprintf("%02d", NumBack)),
    NumBack = factor(NumBack, levels = c("n02", "n01", "n03")),
  ) %>%
  identity()

df0 %>% dim()
[1] 900  74
Code
df2 %>% dim()
[1] 900  74

Models

Code
REML <- TRUE
control <- lme4::lmerControl(optimizer = "Nelder_Mead")

fit.RT    = lmerTest::lmer(formula=RT    ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.TP    = lmerTest::lmer(formula=TP    ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FP    = lmerTest::lmer(formula=FP    ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.TN    = lmerTest::lmer(formula=TN    ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FN    = lmerTest::lmer(formula=FN    ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Accur = lmerTest::lmer(formula=Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Sensi = lmerTest::lmer(formula=Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Speci = lmerTest::lmer(formula=Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Preci = lmerTest::lmer(formula=Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FPR   = lmerTest::lmer(formula=FPR   ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FNR   = lmerTest::lmer(formula=FNR   ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FDR   = lmerTest::lmer(formula=FDR   ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.NPV   = lmerTest::lmer(formula=NPV   ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FOR   = lmerTest::lmer(formula=FOR   ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)

Save Models

Code
readr::write_rds(fit.RT,     file.path(ofd0, "fit.RT.rds"))
readr::write_rds(fit.TP,     file.path(ofd0, "fit.TP.rds"))
readr::write_rds(fit.FP,     file.path(ofd0, "fit.FP.rds"))
readr::write_rds(fit.TN,     file.path(ofd0, "fit.TN.rds"))
readr::write_rds(fit.FN,     file.path(ofd0, "fit.FN.rds"))
readr::write_rds(fit.Accur,  file.path(ofd0, "fit.Accur.rds"))
readr::write_rds(fit.Sensi,  file.path(ofd0, "fit.Sensi.rds"))
readr::write_rds(fit.Speci,  file.path(ofd0, "fit.Speci.rds"))
readr::write_rds(fit.Preci,  file.path(ofd0, "fit.Preci.rds"))
readr::write_rds(fit.FPR,    file.path(ofd0, "fit.FPR.rds"))
readr::write_rds(fit.FNR,    file.path(ofd0, "fit.FNR.rds"))
readr::write_rds(fit.FDR,    file.path(ofd0, "fit.FDR.rds"))
readr::write_rds(fit.NPV,    file.path(ofd0, "fit.NPV.rds"))
readr::write_rds(fit.FOR,    file.path(ofd0, "fit.FOR.rds"))

Read Models

Code
fit.RT    <- readr::read_rds(file.path(ofd0, "fit.RT.rds"))
fit.TP    <- readr::read_rds(file.path(ofd0, "fit.TP.rds"))
fit.FP    <- readr::read_rds(file.path(ofd0, "fit.FP.rds"))
fit.TN    <- readr::read_rds(file.path(ofd0, "fit.TN.rds"))
fit.FN    <- readr::read_rds(file.path(ofd0, "fit.FN.rds"))
fit.Accur <- readr::read_rds(file.path(ofd0, "fit.Accur.rds"))
fit.Sensi <- readr::read_rds(file.path(ofd0, "fit.Sensi.rds"))
fit.Speci <- readr::read_rds(file.path(ofd0, "fit.Speci.rds"))
fit.Preci <- readr::read_rds(file.path(ofd0, "fit.Preci.rds"))
fit.FPR   <- readr::read_rds(file.path(ofd0, "fit.FPR.rds"))
fit.FNR   <- readr::read_rds(file.path(ofd0, "fit.FNR.rds"))
fit.FDR   <- readr::read_rds(file.path(ofd0, "fit.FDR.rds"))
fit.NPV   <- readr::read_rds(file.path(ofd0, "fit.NPV.rds"))
fit.FOR   <- readr::read_rds(file.path(ofd0, "fit.FOR.rds"))

Tabulate Models

View Color-Coded Results

Code
sjPlot::tab_model(
  fit.RT,
  fit.TP, fit.FP, fit.TN, fit.FN,
  fit.Accur,
  fit.Sensi,
  fit.Speci,
  fit.Preci,
  fit.FPR,
  fit.FNR,
  fit.FDR,
  fit.NPV,
  fit.FOR,
  show.reflvl = FALSE,
  show.intercept = TRUE,
  show.p = FALSE,
  wrap.labels = 888,
  p.style = "numeric_stars",
  # file = file.path(ofd0, "all-models-i0002-ALL.html"),
  dv.labels = c(
    "fit.RT",
    "fit.TP", "fit.FP", "fit.TN", "fit.FN",
    "fit.Accur",
    "fit.Sensi",
    "fit.Speci",
    "fit.Preci",
    "fit.FPR",
    "fit.FNR",
    "fit.FDR",
    "fit.NPV",
    "fit.FOR"))
  fit.RT fit.TP fit.FP fit.TN fit.FN fit.Accur fit.Sensi fit.Speci fit.Preci fit.FPR fit.FNR fit.FDR fit.NPV fit.FOR
Predictors Estimates CI Estimates CI Estimates CI Estimates CI Estimates CI Estimates CI Estimates CI Estimates CI Estimates CI Estimates CI Estimates CI Estimates CI Estimates CI Estimates CI
(Intercept) 0.69 *** 0.62 – 0.77 5.52 *** 5.11 – 5.93 0.61 ** 0.20 – 1.02 17.64 *** 16.85 – 18.42 0.54 ** 0.17 – 0.91 0.95 *** 0.93 – 0.98 0.91 *** 0.85 – 0.97 0.97 *** 0.94 – 0.99 0.91 *** 0.85 – 0.96 0.03 ** 0.01 – 0.06 0.09 ** 0.03 – 0.15 0.09 *** 0.04 – 0.15 0.97 *** 0.95 – 0.99 0.03 ** 0.01 – 0.05
Ses [Evening] -0.02 -0.04 – 0.00 0.12 -0.01 – 0.25 0.09 -0.05 – 0.22 0.29 ** 0.08 – 0.50 0.00 -0.11 – 0.12 -0.00 -0.01 – 0.01 0.00 -0.02 – 0.02 -0.00 -0.01 – 0.00 -0.00 -0.02 – 0.01 0.00 -0.00 – 0.01 -0.00 -0.02 – 0.02 0.00 -0.01 – 0.02 -0.00 -0.01 – 0.01 0.00 -0.01 – 0.01
Chrono [Evening] 0.09 * 0.02 – 0.17 0.08 -0.27 – 0.43 0.04 -0.30 – 0.38 0.52 -0.23 – 1.27 0.10 -0.21 – 0.41 -0.00 -0.03 – 0.02 -0.01 -0.06 – 0.04 -0.00 -0.02 – 0.02 -0.01 -0.06 – 0.04 0.00 -0.02 – 0.02 0.01 -0.04 – 0.06 0.01 -0.04 – 0.06 -0.00 -0.02 – 0.01 0.00 -0.01 – 0.02
Run [01] 0.02 -0.01 – 0.04 -0.20 * -0.36 – -0.05 0.10 -0.06 – 0.26 -0.10 -0.36 – 0.16 0.20 ** 0.06 – 0.35 -0.01 * -0.02 – -0.00 -0.03 ** -0.06 – -0.01 -0.01 -0.01 – 0.00 -0.02 -0.04 – 0.00 0.01 -0.00 – 0.01 0.03 ** 0.01 – 0.06 0.02 -0.00 – 0.04 -0.01 ** -0.02 – -0.00 0.01 ** 0.00 – 0.02
Run [03] 0.00 -0.02 – 0.03 -0.01 -0.17 – 0.15 -0.05 -0.21 – 0.11 0.05 -0.20 – 0.31 0.01 -0.13 – 0.15 0.00 -0.01 – 0.01 -0.00 -0.03 – 0.02 0.00 -0.01 – 0.01 0.00 -0.02 – 0.02 -0.00 -0.01 – 0.01 0.00 -0.02 – 0.03 -0.00 -0.02 – 0.02 0.00 -0.01 – 0.01 -0.00 -0.01 – 0.01
Condit [Incongruent] 0.02 -0.01 – 0.06 0.26 * 0.05 – 0.47 -0.01 -0.22 – 0.21 1.05 *** 0.70 – 1.40 0.07 -0.12 – 0.26 0.00 -0.01 – 0.01 -0.01 -0.04 – 0.03 0.00 -0.01 – 0.01 0.00 -0.03 – 0.03 -0.00 -0.01 – 0.01 0.01 -0.03 – 0.04 -0.00 -0.03 – 0.03 -0.00 -0.01 – 0.01 0.00 -0.01 – 0.01
NumBack [n01] -0.15 *** -0.17 – -0.12 0.60 *** 0.44 – 0.76 -0.71 *** -0.87 – -0.55 0.71 *** 0.45 – 0.96 -0.60 *** -0.74 – -0.46 0.05 *** 0.04 – 0.07 0.10 *** 0.08 – 0.13 0.04 *** 0.03 – 0.05 0.11 *** 0.09 – 0.13 -0.04 *** -0.05 – -0.03 -0.10 *** -0.13 – -0.08 -0.11 *** -0.13 – -0.09 0.03 *** 0.02 – 0.04 -0.03 *** -0.04 – -0.02
NumBack [n03] 0.13 *** 0.11 – 0.16 -1.06 *** -1.22 – -0.91 0.62 *** 0.46 – 0.78 -0.62 *** -0.88 – -0.37 1.06 *** 0.92 – 1.21 -0.07 *** -0.08 – -0.06 -0.18 *** -0.20 – -0.15 -0.03 *** -0.04 – -0.03 -0.12 *** -0.14 – -0.10 0.03 *** 0.03 – 0.04 0.18 *** 0.15 – 0.20 0.12 *** 0.10 – 0.14 -0.06 *** -0.06 – -0.05 0.06 *** 0.05 – 0.06
KSS -0.01 -0.02 – 0.01 -0.16 *** -0.25 – -0.07 0.08 -0.01 – 0.17 -0.43 *** -0.58 – -0.28 0.05 -0.03 – 0.13 -0.01 * -0.01 – -0.00 -0.01 -0.02 – 0.00 -0.00 -0.01 – 0.00 -0.01 -0.02 – 0.00 0.00 -0.00 – 0.01 0.01 -0.00 – 0.02 0.01 -0.00 – 0.02 -0.00 -0.01 – 0.00 0.00 -0.00 – 0.01
Gender [m] -0.03 -0.11 – 0.04 0.39 * 0.04 – 0.74 -0.21 -0.55 – 0.12 0.40 -0.35 – 1.15 -0.32 * -0.63 – -0.02 0.02 * 0.00 – 0.05 0.06 * 0.00 – 0.11 0.01 -0.01 – 0.03 0.04 -0.01 – 0.08 -0.01 -0.03 – 0.01 -0.06 * -0.11 – -0.00 -0.04 -0.08 – 0.01 0.02 * 0.00 – 0.03 -0.02 * -0.03 – -0.00
Random Effects
σ2 0.02 0.95 1.00 2.55 0.80 0.00 0.02 0.00 0.02 0.00 0.02 0.02 0.00 0.00
τ00 0.02 Sub 0.34 Sub 0.31 Sub 1.65 Sub 0.25 Sub 0.00 Sub 0.01 Sub 0.00 Sub 0.01 Sub 0.00 Sub 0.01 Sub 0.01 Sub 0.00 Sub 0.00 Sub
ICC 0.40 0.27 0.24 0.39 0.24 0.27 0.23 0.23 0.25 0.23 0.23 0.25 0.24 0.24
N 51 Sub 51 Sub 51 Sub 51 Sub 51 Sub 51 Sub 51 Sub 51 Sub 51 Sub 51 Sub 51 Sub 51 Sub 51 Sub 51 Sub
Observations 898 900 900 900 900 891 891 891 889 891 891 889 891 891
Marginal R2 / Conditional R2 0.272 / 0.561 0.299 / 0.485 0.198 / 0.387 0.122 / 0.466 0.329 / 0.490 0.355 / 0.527 0.335 / 0.491 0.201 / 0.387 0.291 / 0.467 0.201 / 0.387 0.335 / 0.491 0.291 / 0.467 0.344 / 0.502 0.344 / 0.502
* p<0.05   ** p<0.01   *** p<0.001

Plot Models

Code
custom_palette <- c(brewer.pal(9, "Set1"), brewer.pal(5, "Set2"))

line0h <- ggplot2::geom_hline(yintercept = 0, linetype = "dashed", color = "black", lwd = 0.125)
gg88 <- sjPlot::plot_models(
  fit.RT,
  fit.TP, fit.FP, fit.TN, fit.FN,
  fit.Accur,
  fit.Sensi,
  fit.Speci,
  fit.Preci,
  fit.FPR,
  fit.FNR,
  fit.FDR,
  fit.NPV,
  fit.FOR,
  m.labels = c(
    "fit.RT",
    "fit.TP", "fit.FP", "fit.TN", "fit.FN",
    "fit.Accur",
    "fit.Sensi",
    "fit.Speci",
    "fit.Preci",
    "fit.FPR",
    "fit.FNR",
    "fit.FDR",
    "fit.NPV",
    "fit.FOR"),
  legend.title = "Model",
  spacing=1,
  dot.size=1,
  # colors = viridis::viridis(14)
  colors = custom_palette
) + line0h +
  theme(
    text = element_text(size = 24),  # Increase base font size
    axis.title = element_text(size = 24),  # Axis titles
    axis.text = element_text(size = 22),  # Axis text
    legend.title = element_text(size = 24),  # Legend title
    legend.text = element_text(size = 22)  # Legend text
  ) + theme_minimal()

gg88

Factor Reference Levels (reorder for comparison plots)

Code
df2 <- df0 %>%
  mutate(
    Ses = factor(Ses, levels=c("Morning", "Evening")),
    Run = factor(Run, levels=c("01", "02", "03")),
    Chrono = factor(Chrono, levels = c("Morning", "Evening")),
    Condit = factor(Condit, levels = c("Congruent", "Incongruent")),
  ) %>%
  mutate(
    NumBack = as.integer(NumBack),
    NumBack = paste0("n", sprintf("%02d", NumBack)),
    NumBack = factor(NumBack, levels = c("n01", "n02", "n03")),
  ) %>%
  identity()

df0 %>% dim()
[1] 900  74
Code
df2 %>% dim()
[1] 900  74
Code
REML <- TRUE
control <- lme4::lmerControl(optimizer = "Nelder_Mead")

fit.RT    = lmerTest::lmer(formula=RT    ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.TP    = lmerTest::lmer(formula=TP    ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FP    = lmerTest::lmer(formula=FP    ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.TN    = lmerTest::lmer(formula=TN    ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FN    = lmerTest::lmer(formula=FN    ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Accur = lmerTest::lmer(formula=Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Sensi = lmerTest::lmer(formula=Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Speci = lmerTest::lmer(formula=Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Preci = lmerTest::lmer(formula=Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FPR   = lmerTest::lmer(formula=FPR   ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FNR   = lmerTest::lmer(formula=FNR   ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FDR   = lmerTest::lmer(formula=FDR   ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.NPV   = lmerTest::lmer(formula=NPV   ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FOR   = lmerTest::lmer(formula=FOR   ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)

Settings

Code
options(ggeffects_margin = "mean_reference") ## DEFAULT
options(ggeffects_margin = "marginalmeans")
options(ggeffects_margin = "mean_mode")
options(ggeffects_margin = "empirical")
source("./helpers/helpers0.R")

Check Models

Check Model fit.RT

Code
model <- "fit.RT"
fbase <- get_model_info(model, ofd0)
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |  
    Sub)
   Data: df2
Control: control

REML criterion at convergence: -598.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.9882 -0.6340 -0.0920  0.5384  4.4536 

Random effects:
 Groups   Name        Variance Std.Dev.
 Sub      (Intercept) 0.01608  0.1268  
 Residual             0.02447  0.1564  
Number of obs: 898, groups:  Sub, 51

Fixed effects:
                    Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)         0.563010   0.039196 129.924688  14.364   <2e-16 ***
SesEvening         -0.018516   0.010535 844.251873  -1.758   0.0792 .  
ChronoEvening       0.094996   0.037725  48.849313   2.518   0.0151 *  
Run02              -0.015294   0.012783 839.480642  -1.196   0.2319    
Run03              -0.011722   0.012793 839.465851  -0.916   0.3598    
ConditIncongruent   0.024732   0.017516 887.868676   1.412   0.1583    
NumBackn02          0.145712   0.012772 839.465851  11.409   <2e-16 ***
NumBackn03          0.276796   0.012796 839.524887  21.632   <2e-16 ***
KSS                -0.006249   0.007382 881.152930  -0.847   0.3975    
Genderm            -0.032180   0.037596  47.425155  -0.856   0.3963    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# R2 for Mixed Models

  Conditional R2: 0.561
     Marginal R2: 0.272
--------------------------------------------------------------------- 
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.397
  Unadjusted ICC: 0.289
--------------------------------------------------------------------- 
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.397
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of RT

Ses     | Predicted |     95% CI
--------------------------------
Morning |      0.71 | 0.67, 0.75
Evening |      0.69 | 0.65, 0.73
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.69 | 0.65, 0.73 | < .001
Morning |      0.71 | 0.67, 0.75 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning |    -0.02 | -0.04, 0.00 | 0.079

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of RT

Chrono  | Predicted |     95% CI
--------------------------------
Morning |      0.65 | 0.60, 0.70
Evening |      0.75 | 0.70, 0.80
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.75 | 0.70, 0.80 | < .001
Morning |      0.65 | 0.60, 0.70 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          | Contrast |     95% CI |     p
-----------------------------------------------
Evening-Morning |     0.09 | 0.02, 0.17 | 0.012

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of RT

Run | Predicted |     95% CI
----------------------------
01  |      0.71 | 0.67, 0.75
02  |      0.69 | 0.66, 0.73
03  |      0.70 | 0.66, 0.74
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |     95% CI |      p
-------------------------------------
01  |      0.71 | 0.67, 0.75 | < .001
02  |      0.69 | 0.66, 0.73 | < .001
03  |      0.70 | 0.66, 0.74 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   |  Contrast |      95% CI |     p
---------------------------------------
01-02 |      0.02 | -0.01, 0.04 | 0.540
01-03 |      0.01 | -0.01, 0.04 | 0.540
02-03 | -3.57e-03 | -0.03, 0.02 | 0.780

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of RT

Condit      | Predicted |     95% CI
------------------------------------
Congruent   |      0.69 | 0.65, 0.73
Incongruent |      0.71 | 0.67, 0.75
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |     95% CI |      p
---------------------------------------------
Congruent   |      0.69 | 0.65, 0.73 | < .001
Incongruent |      0.71 | 0.67, 0.75 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                | Contrast |      95% CI |     p
------------------------------------------------------
Congruent-Incongruent |    -0.02 | -0.06, 0.01 | 0.158

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of RT

NumBack | Predicted |     95% CI
--------------------------------
n01     |      0.56 | 0.52, 0.60
n02     |      0.71 | 0.67, 0.75
n03     |      0.84 | 0.80, 0.88
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |     95% CI |      p
-----------------------------------------
n01     |      0.56 | 0.52, 0.60 | < .001
n02     |      0.71 | 0.67, 0.75 | < .001
n03     |      0.84 | 0.80, 0.88 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |       95% CI |      p
------------------------------------------
n01-n02 |    -0.15 | -0.17, -0.12 | < .001
n01-n03 |    -0.28 | -0.30, -0.25 | < .001
n02-n03 |    -0.13 | -0.16, -0.11 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of RT

KSS | Predicted |     95% CI
----------------------------
  1 |      0.72 | 0.66, 0.78
  2 |      0.72 | 0.67, 0.76
  3 |      0.71 | 0.67, 0.75
  4 |      0.70 | 0.67, 0.74
  5 |      0.70 | 0.66, 0.73
  6 |      0.69 | 0.65, 0.73
  7 |      0.68 | 0.63, 0.74
  8 |      0.68 | 0.61, 0.74
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope     |      95% CI |     p
-------------------------------
-6.25e-03 | -0.02, 0.01 | 0.398
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope     |      95% CI |     p
-------------------------------
-6.25e-03 | -0.02, 0.01 | 0.398

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of RT

Gender | Predicted |     95% CI
-------------------------------
f      |      0.72 | 0.67, 0.76
m      |      0.68 | 0.63, 0.74
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |     95% CI |      p
----------------------------------------
f      |      0.72 | 0.67, 0.76 | < .001
m      |      0.68 | 0.63, 0.74 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |      95% CI |     p
---------------------------------------
f-m    |     0.03 | -0.04, 0.11 | 0.392

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Check Model fit.TP

Code
model <- "fit.TP"
fbase <- get_model_info(model, ofd0)
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |  
    Sub)
   Data: df2
Control: control

REML criterion at convergence: 2629.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.7373 -0.4808  0.0824  0.5669  2.9202 

Random effects:
 Groups   Name        Variance Std.Dev.
 Sub      (Intercept) 0.3426   0.5853  
 Residual             0.9486   0.9740  
Number of obs: 900, groups:  Sub, 51

Fixed effects:
                   Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)         5.91639    0.21098 184.20095  28.042  < 2e-16 ***
SesEvening          0.12149    0.06545 849.19981   1.856 0.063771 .  
ChronoEvening       0.07988    0.18071  50.24151   0.442 0.660365    
Run02               0.20333    0.07952 842.18071   2.557 0.010736 *  
Run03               0.19333    0.07952 842.18071   2.431 0.015260 *  
ConditIncongruent   0.26092    0.10700 877.30135   2.439 0.014942 *  
NumBackn02         -0.60000    0.07952 842.18071  -7.545 1.17e-13 ***
NumBackn03         -1.66333    0.07952 842.18071 -20.916  < 2e-16 ***
KSS                -0.15927    0.04479 821.61689  -3.556 0.000398 ***
Genderm             0.38904    0.17924  48.07800   2.170 0.034941 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# R2 for Mixed Models

  Conditional R2: 0.485
     Marginal R2: 0.299
--------------------------------------------------------------------- 
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.265
  Unadjusted ICC: 0.186
--------------------------------------------------------------------- 
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.265
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TP

Ses     | Predicted |     95% CI
--------------------------------
Morning |      4.97 | 4.78, 5.15
Evening |      5.09 | 4.90, 5.27
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |     95% CI |      p
-----------------------------------------
Evening |      5.09 | 4.90, 5.27 | < .001
Morning |      4.97 | 4.78, 5.15 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning |     0.12 | -0.01, 0.25 | 0.064

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TP

Chrono  | Predicted |     95% CI
--------------------------------
Morning |      4.99 | 4.74, 5.24
Evening |      5.07 | 4.82, 5.32
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |     95% CI |      p
-----------------------------------------
Evening |      5.07 | 4.82, 5.32 | < .001
Morning |      4.99 | 4.74, 5.24 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning |     0.08 | -0.27, 0.43 | 0.659

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TP

Run | Predicted |     95% CI
----------------------------
01  |      4.90 | 4.70, 5.09
02  |      5.10 | 4.90, 5.30
03  |      5.09 | 4.89, 5.29
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |     95% CI |      p
-------------------------------------
01  |      4.90 | 4.70, 5.09 | < .001
02  |      5.10 | 4.90, 5.30 | < .001
03  |      5.09 | 4.89, 5.29 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   | Contrast |       95% CI |     p
---------------------------------------
01-02 |    -0.20 | -0.36, -0.05 | 0.023
01-03 |    -0.19 | -0.35, -0.04 | 0.023
02-03 |     0.01 | -0.15,  0.17 | 0.900

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TP

Condit      | Predicted |     95% CI
------------------------------------
Congruent   |      4.90 | 4.70, 5.10
Incongruent |      5.16 | 4.96, 5.36
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |     95% CI |      p
---------------------------------------------
Congruent   |      4.90 | 4.70, 5.10 | < .001
Incongruent |      5.16 | 4.96, 5.36 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                | Contrast |       95% CI |     p
-------------------------------------------------------
Congruent-Incongruent |    -0.26 | -0.47, -0.05 | 0.015

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TP

NumBack | Predicted |     95% CI
--------------------------------
n01     |      5.78 | 5.59, 5.98
n02     |      5.18 | 4.99, 5.38
n03     |      4.12 | 3.92, 4.32
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |     95% CI |      p
-----------------------------------------
n01     |      5.78 | 5.59, 5.98 | < .001
n02     |      5.18 | 4.99, 5.38 | < .001
n03     |      4.12 | 3.92, 4.32 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |     95% CI |      p
----------------------------------------
n01-n02 |     0.60 | 0.44, 0.76 | < .001
n01-n03 |     1.66 | 1.51, 1.82 | < .001
n02-n03 |     1.06 | 0.91, 1.22 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TP

KSS | Predicted |     95% CI
----------------------------
  1 |      5.54 | 5.21, 5.87
  2 |      5.38 | 5.12, 5.64
  3 |      5.22 | 5.02, 5.43
  4 |      5.07 | 4.89, 5.24
  5 |      4.91 | 4.72, 5.09
  6 |      4.75 | 4.51, 4.98
  7 |      4.59 | 4.29, 4.89
  8 |      4.43 | 4.05, 4.80
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope |       95% CI |      p
-----------------------------
-0.16 | -0.25, -0.07 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope |       95% CI |      p
-----------------------------
-0.16 | -0.25, -0.07 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TP

Gender | Predicted |     95% CI
-------------------------------
f      |      4.85 | 4.62, 5.09
m      |      5.24 | 4.98, 5.50
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |     95% CI |      p
----------------------------------------
f      |      4.85 | 4.62, 5.09 | < .001
m      |      5.24 | 4.98, 5.50 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |       95% CI |     p
----------------------------------------
f-m    |    -0.39 | -0.74, -0.04 | 0.030

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Check Model fit.FP

Code
model <- "fit.FP"
fbase <- get_model_info(model, ofd0)
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |  
    Sub)
   Data: df2
Control: control

REML criterion at convergence: 2669.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6401 -0.5830 -0.0873  0.3781 11.1668 

Random effects:
 Groups   Name        Variance Std.Dev.
 Sub      (Intercept) 0.3082   0.5551  
 Residual             0.9992   0.9996  
Number of obs: 900, groups:  Sub, 51

Fixed effects:
                    Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)         0.004144   0.209585 199.696091   0.020   0.9842    
SesEvening          0.085549   0.067151 849.987515   1.274   0.2030    
ChronoEvening       0.037219   0.173671  50.526846   0.214   0.8312    
Run02              -0.100000   0.081618 842.274735  -1.225   0.2208    
Run03              -0.153333   0.081618 842.274735  -1.879   0.0606 .  
ConditIncongruent  -0.008700   0.109183 867.454730  -0.080   0.9365    
NumBackn02          0.706667   0.081618 842.274735   8.658   <2e-16 ***
NumBackn03          1.330000   0.081618 842.274735  16.295   <2e-16 ***
KSS                 0.076137   0.045579 791.490956   1.670   0.0952 .  
Genderm            -0.213472   0.171999  48.142738  -1.241   0.2206    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# R2 for Mixed Models

  Conditional R2: 0.387
     Marginal R2: 0.198
--------------------------------------------------------------------- 
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.236
  Unadjusted ICC: 0.189
--------------------------------------------------------------------- 
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.236
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FP

Ses     | Predicted |     95% CI
--------------------------------
Morning |      0.84 | 0.66, 1.01
Evening |      0.92 | 0.74, 1.10
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.92 | 0.74, 1.10 | < .001
Morning |      0.84 | 0.66, 1.01 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning |     0.09 | -0.05, 0.22 | 0.203

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FP

Chrono  | Predicted |     95% CI
--------------------------------
Morning |      0.86 | 0.62, 1.10
Evening |      0.90 | 0.66, 1.14
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.90 | 0.66, 1.14 | < .001
Morning |      0.86 | 0.62, 1.10 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning |     0.04 | -0.30, 0.38 | 0.830

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FP

Run | Predicted |     95% CI
----------------------------
01  |      0.96 | 0.77, 1.15
02  |      0.86 | 0.67, 1.05
03  |      0.81 | 0.62, 1.00
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |     95% CI |      p
-------------------------------------
01  |      0.96 | 0.77, 1.15 | < .001
02  |      0.86 | 0.67, 1.05 | < .001
03  |      0.81 | 0.62, 1.00 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   | Contrast |      95% CI |     p
--------------------------------------
01-02 |     0.10 | -0.06, 0.26 | 0.331
01-03 |     0.15 | -0.01, 0.31 | 0.182
02-03 |     0.05 | -0.11, 0.21 | 0.514

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FP

Condit      | Predicted |     95% CI
------------------------------------
Congruent   |      0.88 | 0.69, 1.08
Incongruent |      0.87 | 0.68, 1.07
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |     95% CI |      p
---------------------------------------------
Congruent   |      0.88 | 0.69, 1.08 | < .001
Incongruent |      0.87 | 0.68, 1.07 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                | Contrast |      95% CI |     p
------------------------------------------------------
Congruent-Incongruent | 8.70e-03 | -0.21, 0.22 | 0.937

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FP

NumBack | Predicted |     95% CI
--------------------------------
n01     |      0.20 | 0.01, 0.39
n02     |      0.91 | 0.72, 1.10
n03     |      1.53 | 1.34, 1.72
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |     95% CI |      p
-----------------------------------------
n01     |      0.20 | 0.01, 0.39 | 0.039 
n02     |      0.91 | 0.72, 1.10 | < .001
n03     |      1.53 | 1.34, 1.72 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |       95% CI |      p
------------------------------------------
n01-n02 |    -0.71 | -0.87, -0.55 | < .001
n01-n03 |    -1.33 | -1.49, -1.17 | < .001
n02-n03 |    -0.62 | -0.78, -0.46 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FP

KSS | Predicted |     95% CI
----------------------------
  1 |      0.63 | 0.30, 0.97
  2 |      0.71 | 0.45, 0.97
  3 |      0.79 | 0.59, 0.98
  4 |      0.86 | 0.69, 1.03
  5 |      0.94 | 0.76, 1.12
  6 |      1.01 | 0.78, 1.24
  7 |      1.09 | 0.79, 1.39
  8 |      1.17 | 0.79, 1.54
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope |      95% CI |     p
---------------------------
0.08  | -0.01, 0.17 | 0.095
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope |      95% CI |     p
---------------------------
0.08  | -0.01, 0.17 | 0.095

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FP

Gender | Predicted |     95% CI
-------------------------------
f      |      0.97 | 0.75, 1.20
m      |      0.76 | 0.51, 1.01
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |     95% CI |      p
----------------------------------------
f      |      0.97 | 0.75, 1.20 | < .001
m      |      0.76 | 0.51, 1.01 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |      95% CI |     p
---------------------------------------
f-m    |     0.21 | -0.12, 0.55 | 0.215

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Check Model fit.TN

Code
model <- "fit.TN"
fbase <- get_model_info(model, ofd0)
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |  
    Sub)
   Data: df2
Control: control

REML criterion at convergence: 3535.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-7.0504 -0.3163  0.0507  0.4047  5.3593 

Random effects:
 Groups   Name        Variance Std.Dev.
 Sub      (Intercept) 1.648    1.284   
 Residual             2.553    1.598   
Number of obs: 900, groups:  Sub, 51

Fixed effects:
                   Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)        18.24224    0.39841 131.96677  45.788  < 2e-16 ***
SesEvening          0.29218    0.10747 846.46183   2.719  0.00669 ** 
ChronoEvening       0.51673    0.38220  49.13531   1.352  0.18257    
Run02               0.10000    0.13046 841.73880   0.766  0.44360    
Run03               0.15333    0.13046 841.73880   1.175  0.24021    
ConditIncongruent   1.05224    0.17855 889.94626   5.893 5.36e-09 ***
NumBackn02         -0.70667    0.13046 841.73880  -5.417 7.93e-08 ***
NumBackn03         -1.33000    0.13046 841.73880 -10.194  < 2e-16 ***
KSS                -0.43029    0.07532 882.23254  -5.713 1.52e-08 ***
Genderm             0.39981    0.38088  47.70375   1.050  0.29914    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# R2 for Mixed Models

  Conditional R2: 0.466
     Marginal R2: 0.122
--------------------------------------------------------------------- 
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.392
  Unadjusted ICC: 0.344
--------------------------------------------------------------------- 
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.392
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TN

Ses     | Predicted |       95% CI
----------------------------------
Morning |     16.80 | 16.41, 17.18
Evening |     17.09 | 16.70, 17.47
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |       95% CI |      p
-------------------------------------------
Evening |     17.09 | 16.70, 17.47 | < .001
Morning |     16.80 | 16.41, 17.18 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             | Contrast |     95% CI |     p
-----------------------------------------------
Evening-Morning |     0.29 | 0.08, 0.50 | 0.007

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TN

Chrono  | Predicted |       95% CI
----------------------------------
Morning |     16.68 | 16.16, 17.20
Evening |     17.20 | 16.67, 17.73
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |       95% CI |      p
-------------------------------------------
Evening |     17.20 | 16.67, 17.73 | < .001
Morning |     16.68 | 16.16, 17.20 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning |     0.52 | -0.23, 1.27 | 0.177

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TN

Run | Predicted |       95% CI
------------------------------
01  |     16.86 | 16.46, 17.25
02  |     16.96 | 16.56, 17.35
03  |     17.01 | 16.61, 17.41
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |       95% CI |      p
---------------------------------------
01  |     16.86 | 16.46, 17.25 | < .001
02  |     16.96 | 16.56, 17.35 | < .001
03  |     17.01 | 16.61, 17.41 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   | Contrast |      95% CI |     p
--------------------------------------
01-02 |    -0.10 | -0.36, 0.16 | 0.665
01-03 |    -0.15 | -0.41, 0.10 | 0.665
02-03 |    -0.05 | -0.31, 0.20 | 0.683

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TN

Condit      | Predicted |       95% CI
--------------------------------------
Congruent   |     16.41 | 16.01, 16.82
Incongruent |     17.47 | 17.06, 17.87
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |       95% CI |      p
-----------------------------------------------
Congruent   |     16.41 | 16.01, 16.82 | < .001
Incongruent |     17.47 | 17.06, 17.87 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                | Contrast |       95% CI |      p
--------------------------------------------------------
Congruent-Incongruent |    -1.05 | -1.40, -0.70 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TN

NumBack | Predicted |       95% CI
----------------------------------
n01     |     17.62 | 17.22, 18.02
n02     |     16.91 | 16.52, 17.31
n03     |     16.29 | 15.89, 16.69
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |       95% CI |      p
-------------------------------------------
n01     |     17.62 | 17.22, 18.02 | < .001
n02     |     16.91 | 16.52, 17.31 | < .001
n03     |     16.29 | 15.89, 16.69 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |     95% CI |      p
----------------------------------------
n01-n02 |     0.71 | 0.45, 0.96 | < .001
n01-n03 |     1.33 | 1.07, 1.59 | < .001
n02-n03 |     0.62 | 0.37, 0.88 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TN

KSS | Predicted |       95% CI
------------------------------
  1 |     18.33 | 17.73, 18.93
  2 |     17.90 | 17.41, 18.39
  3 |     17.47 | 17.06, 17.88
  4 |     17.04 | 16.67, 17.41
  5 |     16.61 | 16.22, 17.00
  6 |     16.18 | 15.73, 16.63
  7 |     15.75 | 15.20, 16.30
  8 |     15.32 | 14.65, 15.99
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope |       95% CI |      p
-----------------------------
-0.43 | -0.58, -0.28 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope |       95% CI |      p
-----------------------------
-0.43 | -0.58, -0.28 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of TN

Gender | Predicted |       95% CI
---------------------------------
f      |     16.76 | 16.26, 17.26
m      |     17.16 | 16.61, 17.71
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |       95% CI |      p
------------------------------------------
f      |     16.76 | 16.26, 17.26 | < .001
m      |     17.16 | 16.61, 17.71 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |      95% CI |     p
---------------------------------------
f-m    |    -0.40 | -1.15, 0.35 | 0.294

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Check Model fit.FN

Code
model <- "fit.FN"
fbase <- get_model_info(model, ofd0)
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |  
    Sub)
   Data: df2
Control: control

REML criterion at convergence: 2475.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5049 -0.6100 -0.0826  0.4990  3.6960 

Random effects:
 Groups   Name        Variance Std.Dev.
 Sub      (Intercept) 0.2539   0.5039  
 Residual             0.8030   0.8961  
Number of obs: 900, groups:  Sub, 51

Fixed effects:
                    Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)         0.139069   0.188825 198.277405   0.736  0.46230    
SesEvening          0.003268   0.060201 850.117631   0.054  0.95672    
ChronoEvening       0.098384   0.157291  50.799611   0.625  0.53445    
Run02              -0.203333   0.073167 842.560847  -2.779  0.00557 ** 
Run03              -0.193333   0.073167 842.560847  -2.642  0.00839 ** 
ConditIncongruent   0.068652   0.097970 869.353647   0.701  0.48365    
NumBackn02          0.600000   0.073167 842.560847   8.200  8.9e-16 ***
NumBackn03          1.663333   0.073167 842.560847  22.733  < 2e-16 ***
KSS                 0.050328   0.040917 797.187750   1.230  0.21906    
Genderm            -0.324170   0.155816  48.436707  -2.080  0.04280 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# R2 for Mixed Models

  Conditional R2: 0.490
     Marginal R2: 0.329
--------------------------------------------------------------------- 
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.240
  Unadjusted ICC: 0.161
--------------------------------------------------------------------- 
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.240
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FN

Ses     | Predicted |     95% CI
--------------------------------
Morning |      0.91 | 0.75, 1.07
Evening |      0.91 | 0.75, 1.07
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.91 | 0.75, 1.07 | < .001
Morning |      0.91 | 0.75, 1.07 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning | 3.27e-03 | -0.11, 0.12 | 0.957

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FN

Chrono  | Predicted |     95% CI
--------------------------------
Morning |      0.86 | 0.65, 1.08
Evening |      0.96 | 0.74, 1.18
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.96 | 0.74, 1.18 | < .001
Morning |      0.86 | 0.65, 1.08 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning |     0.10 | -0.21, 0.41 | 0.532

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FN

Run | Predicted |     95% CI
----------------------------
01  |      1.04 | 0.87, 1.22
02  |      0.84 | 0.67, 1.01
03  |      0.85 | 0.68, 1.02
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |     95% CI |      p
-------------------------------------
01  |      1.04 | 0.87, 1.22 | < .001
02  |      0.84 | 0.67, 1.01 | < .001
03  |      0.85 | 0.68, 1.02 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   | Contrast |      95% CI |     p
--------------------------------------
01-02 |     0.20 |  0.06, 0.35 | 0.013
01-03 |     0.19 |  0.05, 0.34 | 0.013
02-03 |    -0.01 | -0.15, 0.13 | 0.891

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FN

Condit      | Predicted |     95% CI
------------------------------------
Congruent   |      0.88 | 0.70, 1.06
Incongruent |      0.95 | 0.77, 1.12
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |     95% CI |      p
---------------------------------------------
Congruent   |      0.88 | 0.70, 1.06 | < .001
Incongruent |      0.95 | 0.77, 1.12 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                | Contrast |      95% CI |     p
------------------------------------------------------
Congruent-Incongruent |    -0.07 | -0.26, 0.12 | 0.484

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FN

NumBack | Predicted |      95% CI
---------------------------------
n01     |      0.16 | -0.02, 0.33
n02     |      0.76 |  0.58, 0.93
n03     |      1.82 |  1.65, 1.99
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |      95% CI |      p
------------------------------------------
n01     |      0.16 | -0.02, 0.33 | 0.074 
n02     |      0.76 |  0.58, 0.93 | < .001
n03     |      1.82 |  1.65, 1.99 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |       95% CI |      p
------------------------------------------
n01-n02 |    -0.60 | -0.74, -0.46 | < .001
n01-n03 |    -1.66 | -1.81, -1.52 | < .001
n02-n03 |    -1.06 | -1.21, -0.92 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FN

KSS | Predicted |     95% CI
----------------------------
  1 |      0.75 | 0.45, 1.05
  2 |      0.80 | 0.57, 1.03
  3 |      0.85 | 0.67, 1.03
  4 |      0.90 | 0.75, 1.05
  5 |      0.95 | 0.79, 1.11
  6 |      1.00 | 0.79, 1.21
  7 |      1.05 | 0.78, 1.32
  8 |      1.10 | 0.76, 1.44
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope |      95% CI |     p
---------------------------
0.05  | -0.03, 0.13 | 0.219
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope |      95% CI |     p
---------------------------
0.05  | -0.03, 0.13 | 0.219

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | 
    Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FN

Gender | Predicted |     95% CI
-------------------------------
f      |      1.06 | 0.85, 1.26
m      |      0.73 | 0.51, 0.96
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |     95% CI |      p
----------------------------------------
f      |      1.06 | 0.85, 1.26 | < .001
m      |      0.73 | 0.51, 0.96 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |     95% CI |     p
--------------------------------------
f-m    |     0.32 | 0.02, 0.63 | 0.038

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Check Model fit.Accur

Code
model <- "fit.Accur"
fbase <- get_model_info(model, ofd0)
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +  
    (1 | Sub)
   Data: df2
Control: control

REML criterion at convergence: -2238.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-7.4909 -0.4963  0.1001  0.6073  2.4745 

Random effects:
 Groups   Name        Variance Std.Dev.
 Sub      (Intercept) 0.001416 0.03762 
 Residual             0.003888 0.06236 
Number of obs: 891, groups:  Sub, 51

Fixed effects:
                    Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)        9.952e-01  1.354e-02  1.830e+02  73.501  < 2e-16 ***
SesEvening        -2.813e-03  4.232e-03  8.450e+02  -0.665  0.50647    
ChronoEvening     -4.329e-03  1.164e-02  5.054e+01  -0.372  0.71154    
Run02              1.277e-02  5.117e-03  8.333e+02   2.495  0.01279 *  
Run03              1.459e-02  5.117e-03  8.333e+02   2.851  0.00446 ** 
ConditIncongruent  1.720e-06  7.031e-03  8.583e+02   0.000  0.99980    
NumBackn02        -5.499e-02  5.117e-03  8.333e+02 -10.747  < 2e-16 ***
NumBackn03        -1.260e-01  5.117e-03  8.333e+02 -24.620  < 2e-16 ***
KSS               -6.127e-03  2.916e-03  8.003e+02  -2.101  0.03593 *  
Genderm            2.280e-02  1.152e-02  4.805e+01   1.980  0.05350 .  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# R2 for Mixed Models

  Conditional R2: 0.527
     Marginal R2: 0.355
--------------------------------------------------------------------- 
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.267
  Unadjusted ICC: 0.172
--------------------------------------------------------------------- 
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.267
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Accur

Ses     | Predicted |     95% CI
--------------------------------
Morning |      0.93 | 0.91, 0.94
Evening |      0.92 | 0.91, 0.94
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.92 | 0.91, 0.94 | < .001
Morning |      0.93 | 0.91, 0.94 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             |  Contrast |      95% CI |     p
-------------------------------------------------
Evening-Morning | -2.81e-03 | -0.01, 0.01 | 0.506

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Accur

Chrono  | Predicted |     95% CI
--------------------------------
Morning |      0.93 | 0.91, 0.94
Evening |      0.92 | 0.91, 0.94
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.92 | 0.91, 0.94 | < .001
Morning |      0.93 | 0.91, 0.94 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          |  Contrast |      95% CI |     p
-------------------------------------------------
Evening-Morning | -4.33e-03 | -0.03, 0.02 | 0.710

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Accur

Run | Predicted |     95% CI
----------------------------
01  |      0.92 | 0.90, 0.93
02  |      0.93 | 0.92, 0.94
03  |      0.93 | 0.92, 0.94
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |     95% CI |      p
-------------------------------------
01  |      0.92 | 0.90, 0.93 | < .001
02  |      0.93 | 0.92, 0.94 | < .001
03  |      0.93 | 0.92, 0.94 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   |  Contrast |       95% CI |     p
----------------------------------------
01-02 |     -0.01 | -0.02,  0.00 | 0.019
01-03 |     -0.01 | -0.02,  0.00 | 0.013
02-03 | -1.82e-03 | -0.01,  0.01 | 0.722

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Accur

Condit      | Predicted |     95% CI
------------------------------------
Congruent   |      0.92 | 0.91, 0.94
Incongruent |      0.92 | 0.91, 0.94
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |     95% CI |      p
---------------------------------------------
Congruent   |      0.92 | 0.91, 0.94 | < .001
Incongruent |      0.92 | 0.91, 0.94 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                |  Contrast |      95% CI |      p
--------------------------------------------------------
Congruent-Incongruent | -1.72e-06 | -0.01, 0.01 | > .999

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Accur

NumBack | Predicted |     95% CI
--------------------------------
n01     |      0.98 | 0.97, 1.00
n02     |      0.93 | 0.92, 0.94
n03     |      0.86 | 0.85, 0.87
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |     95% CI |      p
-----------------------------------------
n01     |      0.98 | 0.97, 1.00 | < .001
n02     |      0.93 | 0.92, 0.94 | < .001
n03     |      0.86 | 0.85, 0.87 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |     95% CI |      p
----------------------------------------
n01-n02 |     0.05 | 0.04, 0.07 | < .001
n01-n03 |     0.13 | 0.12, 0.14 | < .001
n02-n03 |     0.07 | 0.06, 0.08 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Accur

KSS | Predicted |     95% CI
----------------------------
  1 |      0.94 | 0.92, 0.97
  2 |      0.94 | 0.92, 0.96
  3 |      0.93 | 0.92, 0.95
  4 |      0.93 | 0.91, 0.94
  5 |      0.92 | 0.91, 0.93
  6 |      0.91 | 0.90, 0.93
  7 |      0.91 | 0.89, 0.93
  8 |      0.90 | 0.88, 0.93
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope     |       95% CI |     p
--------------------------------
-6.13e-03 | -0.01,  0.00 | 0.036
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope     |       95% CI |     p
--------------------------------
-6.13e-03 | -0.01,  0.00 | 0.036

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Accur

Gender | Predicted |     95% CI
-------------------------------
f      |      0.91 | 0.90, 0.93
m      |      0.94 | 0.92, 0.95
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |     95% CI |      p
----------------------------------------
f      |      0.91 | 0.90, 0.93 | < .001
m      |      0.94 | 0.92, 0.95 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |       95% CI |     p
----------------------------------------
f-m    |    -0.02 | -0.05,  0.00 | 0.048

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Check Model fit.Sensi

Code
model <- "fit.Sensi"
fbase <- get_model_info(model, ofd0)
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +  
    (1 | Sub)
   Data: df2
Control: control

REML criterion at convergence: -704.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.6616 -0.4992  0.0791  0.6101  2.5049 

Random effects:
 Groups   Name        Variance Std.Dev.
 Sub      (Intercept) 0.006864 0.08285 
 Residual             0.022360 0.14953 
Number of obs: 891, groups:  Sub, 51

Fixed effects:
                    Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)         0.978820   0.031344 200.304268  31.228  < 2e-16 ***
SesEvening          0.001712   0.010144 846.426854   0.169  0.86597    
ChronoEvening      -0.013169   0.026008  51.086968  -0.506  0.61478    
Run02               0.034231   0.012271 833.660412   2.790  0.00540 ** 
Run03               0.032548   0.012271 833.660412   2.652  0.00814 ** 
ConditIncongruent  -0.005493   0.016743 843.615673  -0.328  0.74293    
NumBackn02         -0.101010   0.012271 833.660412  -8.232 7.09e-16 ***
NumBackn03         -0.280022   0.012271 833.660412 -22.820  < 2e-16 ***
KSS                -0.010352   0.006924 765.128571  -1.495  0.13533    
Genderm             0.055245   0.025690  48.327741   2.150  0.03655 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# R2 for Mixed Models

  Conditional R2: 0.491
     Marginal R2: 0.335
--------------------------------------------------------------------- 
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.235
  Unadjusted ICC: 0.156
--------------------------------------------------------------------- 
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.235
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Sensi

Ses     | Predicted |     95% CI
--------------------------------
Morning |      0.85 | 0.82, 0.87
Evening |      0.85 | 0.82, 0.87
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.85 | 0.82, 0.87 | < .001
Morning |      0.85 | 0.82, 0.87 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning | 1.71e-03 | -0.02, 0.02 | 0.866

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Sensi

Chrono  | Predicted |     95% CI
--------------------------------
Morning |      0.85 | 0.82, 0.89
Evening |      0.84 | 0.80, 0.88
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.84 | 0.80, 0.88 | < .001
Morning |      0.85 | 0.82, 0.89 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning |    -0.01 | -0.06, 0.04 | 0.613

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Sensi

Run | Predicted |     95% CI
----------------------------
01  |      0.82 | 0.80, 0.85
02  |      0.86 | 0.83, 0.89
03  |      0.86 | 0.83, 0.89
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |     95% CI |      p
-------------------------------------
01  |      0.82 | 0.80, 0.85 | < .001
02  |      0.86 | 0.83, 0.89 | < .001
03  |      0.86 | 0.83, 0.89 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   | Contrast |       95% CI |     p
---------------------------------------
01-02 |    -0.03 | -0.06, -0.01 | 0.012
01-03 |    -0.03 | -0.06, -0.01 | 0.012
02-03 | 1.68e-03 | -0.02,  0.03 | 0.891

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Sensi

Condit      | Predicted |     95% CI
------------------------------------
Congruent   |      0.85 | 0.82, 0.88
Incongruent |      0.84 | 0.81, 0.87
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |     95% CI |      p
---------------------------------------------
Congruent   |      0.85 | 0.82, 0.88 | < .001
Incongruent |      0.84 | 0.81, 0.87 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                | Contrast |      95% CI |     p
------------------------------------------------------
Congruent-Incongruent | 5.49e-03 | -0.03, 0.04 | 0.743

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Sensi

NumBack | Predicted |     95% CI
--------------------------------
n01     |      0.97 | 0.95, 1.00
n02     |      0.87 | 0.84, 0.90
n03     |      0.69 | 0.67, 0.72
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |     95% CI |      p
-----------------------------------------
n01     |      0.97 | 0.95, 1.00 | < .001
n02     |      0.87 | 0.84, 0.90 | < .001
n03     |      0.69 | 0.67, 0.72 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |     95% CI |      p
----------------------------------------
n01-n02 |     0.10 | 0.08, 0.13 | < .001
n01-n03 |     0.28 | 0.26, 0.30 | < .001
n02-n03 |     0.18 | 0.15, 0.20 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Sensi

KSS | Predicted |     95% CI
----------------------------
  1 |      0.88 | 0.83, 0.93
  2 |      0.87 | 0.83, 0.91
  3 |      0.86 | 0.83, 0.89
  4 |      0.85 | 0.82, 0.87
  5 |      0.84 | 0.81, 0.87
  6 |      0.83 | 0.79, 0.86
  7 |      0.82 | 0.77, 0.86
  8 |      0.81 | 0.75, 0.86
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope |      95% CI |     p
---------------------------
-0.01 | -0.02, 0.00 | 0.135
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope |      95% CI |     p
---------------------------
-0.01 | -0.02, 0.00 | 0.135

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Sensi

Gender | Predicted |     95% CI
-------------------------------
f      |      0.82 | 0.79, 0.86
m      |      0.88 | 0.84, 0.91
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |     95% CI |      p
----------------------------------------
f      |      0.82 | 0.79, 0.86 | < .001
m      |      0.88 | 0.84, 0.91 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |       95% CI |     p
----------------------------------------
f-m    |    -0.06 | -0.11,  0.00 | 0.032

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Check Model fit.Speci

Code
model <- "fit.Speci"
fbase <- get_model_info(model, ofd0)
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +  
    (1 | Sub)
   Data: df2
Control: control

REML criterion at convergence: -2444.1

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-11.1354  -0.3716   0.0852   0.5787   2.6304 

Random effects:
 Groups   Name        Variance  Std.Dev.
 Sub      (Intercept) 0.0009384 0.03063 
 Residual             0.0031065 0.05574 
Number of obs: 891, groups:  Sub, 51

Fixed effects:
                    Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)         1.000223   0.011646 200.519749  85.885   <2e-16 ***
SesEvening         -0.004324   0.003781 846.259686  -1.144   0.2531    
ChronoEvening      -0.001468   0.009631  50.703456  -0.152   0.8795    
Run02               0.005612   0.004574 833.284313   1.227   0.2202    
Run03               0.008605   0.004574 833.284313   1.881   0.0603 .  
ConditIncongruent   0.001576   0.006236 841.690833   0.253   0.8006    
NumBackn02         -0.039656   0.004574 833.284313  -8.670   <2e-16 ***
NumBackn03         -0.074635   0.004574 833.284313 -16.318   <2e-16 ***
KSS                -0.004583   0.002578 760.574147  -1.777   0.0759 .  
Genderm             0.012095   0.009512  47.941183   1.272   0.2097    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# R2 for Mixed Models

  Conditional R2: 0.387
     Marginal R2: 0.201
--------------------------------------------------------------------- 
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.232
  Unadjusted ICC: 0.185
--------------------------------------------------------------------- 
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.232
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Speci

Ses     | Predicted |     95% CI
--------------------------------
Morning |      0.95 | 0.94, 0.96
Evening |      0.95 | 0.94, 0.96
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.95 | 0.94, 0.96 | < .001
Morning |      0.95 | 0.94, 0.96 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             |  Contrast |      95% CI |     p
-------------------------------------------------
Evening-Morning | -4.32e-03 | -0.01, 0.00 | 0.253

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Speci

Chrono  | Predicted |     95% CI
--------------------------------
Morning |      0.95 | 0.94, 0.96
Evening |      0.95 | 0.94, 0.96
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.95 | 0.94, 0.96 | < .001
Morning |      0.95 | 0.94, 0.96 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          |  Contrast |      95% CI |     p
-------------------------------------------------
Evening-Morning | -1.47e-03 | -0.02, 0.02 | 0.879

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Speci

Run | Predicted |     95% CI
----------------------------
01  |      0.95 | 0.94, 0.96
02  |      0.95 | 0.94, 0.96
03  |      0.95 | 0.94, 0.97
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |     95% CI |      p
-------------------------------------
01  |      0.95 | 0.94, 0.96 | < .001
02  |      0.95 | 0.94, 0.96 | < .001
03  |      0.95 | 0.94, 0.97 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   |  Contrast |      95% CI |     p
---------------------------------------
01-02 | -5.61e-03 | -0.01, 0.00 | 0.330
01-03 | -8.60e-03 | -0.02, 0.00 | 0.181
02-03 | -2.99e-03 | -0.01, 0.01 | 0.513

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Speci

Condit      | Predicted |     95% CI
------------------------------------
Congruent   |      0.95 | 0.94, 0.96
Incongruent |      0.95 | 0.94, 0.96
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |     95% CI |      p
---------------------------------------------
Congruent   |      0.95 | 0.94, 0.96 | < .001
Incongruent |      0.95 | 0.94, 0.96 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                |  Contrast |      95% CI |     p
-------------------------------------------------------
Congruent-Incongruent | -1.58e-03 | -0.01, 0.01 | 0.801

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Speci

NumBack | Predicted |     95% CI
--------------------------------
n01     |      0.99 | 0.98, 1.00
n02     |      0.95 | 0.94, 0.96
n03     |      0.91 | 0.90, 0.92
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |     95% CI |      p
-----------------------------------------
n01     |      0.99 | 0.98, 1.00 | < .001
n02     |      0.95 | 0.94, 0.96 | < .001
n03     |      0.91 | 0.90, 0.92 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |     95% CI |      p
----------------------------------------
n01-n02 |     0.04 | 0.03, 0.05 | < .001
n01-n03 |     0.07 | 0.07, 0.08 | < .001
n02-n03 |     0.03 | 0.03, 0.04 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Speci

KSS | Predicted |     95% CI
----------------------------
  1 |      0.97 | 0.95, 0.98
  2 |      0.96 | 0.95, 0.98
  3 |      0.96 | 0.95, 0.97
  4 |      0.95 | 0.94, 0.96
  5 |      0.95 | 0.94, 0.96
  6 |      0.94 | 0.93, 0.96
  7 |      0.94 | 0.92, 0.95
  8 |      0.93 | 0.91, 0.95
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope     |      95% CI |     p
-------------------------------
-4.58e-03 | -0.01, 0.00 | 0.076
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope     |      95% CI |     p
-------------------------------
-4.58e-03 | -0.01, 0.00 | 0.076

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Speci

Gender | Predicted |     95% CI
-------------------------------
f      |      0.95 | 0.93, 0.96
m      |      0.96 | 0.94, 0.97
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |     95% CI |      p
----------------------------------------
f      |      0.95 | 0.93, 0.96 | < .001
m      |      0.96 | 0.94, 0.97 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |      95% CI |     p
---------------------------------------
f-m    |    -0.01 | -0.03, 0.01 | 0.204

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Check Model fit.Preci

Code
model <- "fit.Preci"
fbase <- get_model_info(model, ofd0)
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +  
    (1 | Sub)
   Data: df2
Control: control

REML criterion at convergence: -903.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.6505 -0.4965  0.0325  0.6102  2.8078 

Random effects:
 Groups   Name        Variance Std.Dev.
 Sub      (Intercept) 0.005836 0.0764  
 Residual             0.017732 0.1332  
Number of obs: 889, groups:  Sub, 51

Fixed effects:
                    Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)         0.999561   0.028312 191.411034  35.305   <2e-16 ***
SesEvening         -0.004639   0.009048 843.623958  -0.513   0.6083    
ChronoEvening      -0.011303   0.023841  50.340107  -0.474   0.6375    
Run02               0.019549   0.010937 831.034887   1.787   0.0742 .  
Run03               0.022288   0.010946 831.008107   2.036   0.0420 *  
ConditIncongruent   0.001412   0.014984 848.317176   0.094   0.9250    
NumBackn02         -0.113440   0.010927 831.008107 -10.381   <2e-16 ***
NumBackn03         -0.232108   0.010948 831.114979 -21.201   <2e-16 ***
KSS                -0.010776   0.006196 778.171838  -1.739   0.0824 .  
Genderm             0.037181   0.023564  47.677332   1.578   0.1212    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# R2 for Mixed Models

  Conditional R2: 0.467
     Marginal R2: 0.291
--------------------------------------------------------------------- 
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.248
  Unadjusted ICC: 0.176
--------------------------------------------------------------------- 
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.248
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Preci

Ses     | Predicted |     95% CI
--------------------------------
Morning |      0.86 | 0.84, 0.89
Evening |      0.86 | 0.84, 0.88
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.86 | 0.84, 0.88 | < .001
Morning |      0.86 | 0.84, 0.89 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             |  Contrast |      95% CI |     p
-------------------------------------------------
Evening-Morning | -4.64e-03 | -0.02, 0.01 | 0.608

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Preci

Chrono  | Predicted |     95% CI
--------------------------------
Morning |      0.87 | 0.84, 0.90
Evening |      0.86 | 0.82, 0.89
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.86 | 0.82, 0.89 | < .001
Morning |      0.87 | 0.84, 0.90 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning |    -0.01 | -0.06, 0.04 | 0.636

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Preci

Run | Predicted |     95% CI
----------------------------
01  |      0.85 | 0.82, 0.87
02  |      0.87 | 0.84, 0.89
03  |      0.87 | 0.84, 0.90
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |     95% CI |      p
-------------------------------------
01  |      0.85 | 0.82, 0.87 | < .001
02  |      0.87 | 0.84, 0.89 | < .001
03  |      0.87 | 0.84, 0.90 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   |  Contrast |       95% CI |     p
----------------------------------------
01-02 |     -0.02 | -0.04,  0.00 | 0.111
01-03 |     -0.02 | -0.04,  0.00 | 0.111
02-03 | -2.74e-03 | -0.02,  0.02 | 0.802

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Preci

Condit      | Predicted |     95% CI
------------------------------------
Congruent   |      0.86 | 0.83, 0.89
Incongruent |      0.86 | 0.84, 0.89
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |     95% CI |      p
---------------------------------------------
Congruent   |      0.86 | 0.83, 0.89 | < .001
Incongruent |      0.86 | 0.84, 0.89 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                |  Contrast |      95% CI |     p
-------------------------------------------------------
Congruent-Incongruent | -1.41e-03 | -0.03, 0.03 | 0.925

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Preci

NumBack | Predicted |     95% CI
--------------------------------
n01     |      0.98 | 0.95, 1.00
n02     |      0.86 | 0.84, 0.89
n03     |      0.74 | 0.72, 0.77
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |     95% CI |      p
-----------------------------------------
n01     |      0.98 | 0.95, 1.00 | < .001
n02     |      0.86 | 0.84, 0.89 | < .001
n03     |      0.74 | 0.72, 0.77 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |     95% CI |      p
----------------------------------------
n01-n02 |     0.11 | 0.09, 0.13 | < .001
n01-n03 |     0.23 | 0.21, 0.25 | < .001
n02-n03 |     0.12 | 0.10, 0.14 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Preci

KSS | Predicted |     95% CI
----------------------------
  1 |      0.90 | 0.85, 0.94
  2 |      0.89 | 0.85, 0.92
  3 |      0.88 | 0.85, 0.90
  4 |      0.86 | 0.84, 0.89
  5 |      0.85 | 0.83, 0.88
  6 |      0.84 | 0.81, 0.87
  7 |      0.83 | 0.79, 0.87
  8 |      0.82 | 0.77, 0.87
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope |      95% CI |     p
---------------------------
-0.01 | -0.02, 0.00 | 0.082
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope |      95% CI |     p
---------------------------
-0.01 | -0.02, 0.00 | 0.082

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of Preci

Gender | Predicted |     95% CI
-------------------------------
f      |      0.85 | 0.81, 0.88
m      |      0.88 | 0.85, 0.92
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |     95% CI |      p
----------------------------------------
f      |      0.85 | 0.81, 0.88 | < .001
m      |      0.88 | 0.85, 0.92 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |      95% CI |     p
---------------------------------------
f-m    |    -0.04 | -0.08, 0.01 | 0.115

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Check Model fit.FPR

Code
model <- "fit.FPR"
fbase <- get_model_info(model, ofd0)
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +  
    (1 | Sub)
   Data: df2
Control: control

REML criterion at convergence: -2444.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6304 -0.5787 -0.0852  0.3716 11.1354 

Random effects:
 Groups   Name        Variance  Std.Dev.
 Sub      (Intercept) 0.0009384 0.03063 
 Residual             0.0031065 0.05574 
Number of obs: 891, groups:  Sub, 51

Fixed effects:
                    Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       -2.235e-04  1.165e-02  2.005e+02  -0.019   0.9847    
SesEvening         4.324e-03  3.781e-03  8.463e+02   1.144   0.2531    
ChronoEvening      1.468e-03  9.631e-03  5.070e+01   0.152   0.8795    
Run02             -5.612e-03  4.574e-03  8.333e+02  -1.227   0.2202    
Run03             -8.605e-03  4.574e-03  8.333e+02  -1.881   0.0603 .  
ConditIncongruent -1.576e-03  6.236e-03  8.417e+02  -0.253   0.8006    
NumBackn02         3.966e-02  4.574e-03  8.333e+02   8.670   <2e-16 ***
NumBackn03         7.464e-02  4.574e-03  8.333e+02  16.318   <2e-16 ***
KSS                4.583e-03  2.578e-03  7.606e+02   1.777   0.0759 .  
Genderm           -1.209e-02  9.512e-03  4.794e+01  -1.272   0.2097    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# R2 for Mixed Models

  Conditional R2: 0.387
     Marginal R2: 0.201
--------------------------------------------------------------------- 
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.232
  Unadjusted ICC: 0.185
--------------------------------------------------------------------- 
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.232
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FPR

Ses     | Predicted |     95% CI
--------------------------------
Morning |      0.05 | 0.04, 0.06
Evening |      0.05 | 0.04, 0.06
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.05 | 0.04, 0.06 | < .001
Morning |      0.05 | 0.04, 0.06 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning | 4.32e-03 |  0.00, 0.01 | 0.253

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FPR

Chrono  | Predicted |     95% CI
--------------------------------
Morning |      0.05 | 0.04, 0.06
Evening |      0.05 | 0.04, 0.06
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.05 | 0.04, 0.06 | < .001
Morning |      0.05 | 0.04, 0.06 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning | 1.47e-03 | -0.02, 0.02 | 0.879

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FPR

Run | Predicted |     95% CI
----------------------------
01  |      0.05 | 0.04, 0.06
02  |      0.05 | 0.04, 0.06
03  |      0.05 | 0.03, 0.06
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |     95% CI |      p
-------------------------------------
01  |      0.05 | 0.04, 0.06 | < .001
02  |      0.05 | 0.04, 0.06 | < .001
03  |      0.05 | 0.03, 0.06 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   | Contrast |      95% CI |     p
--------------------------------------
01-02 | 5.61e-03 |  0.00, 0.01 | 0.330
01-03 | 8.60e-03 |  0.00, 0.02 | 0.181
02-03 | 2.99e-03 | -0.01, 0.01 | 0.513

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FPR

Condit      | Predicted |     95% CI
------------------------------------
Congruent   |      0.05 | 0.04, 0.06
Incongruent |      0.05 | 0.04, 0.06
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |     95% CI |      p
---------------------------------------------
Congruent   |      0.05 | 0.04, 0.06 | < .001
Incongruent |      0.05 | 0.04, 0.06 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                | Contrast |      95% CI |     p
------------------------------------------------------
Congruent-Incongruent | 1.58e-03 | -0.01, 0.01 | 0.801

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FPR

NumBack | Predicted |     95% CI
--------------------------------
n01     |      0.01 | 0.00, 0.02
n02     |      0.05 | 0.04, 0.06
n03     |      0.09 | 0.08, 0.10
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |     95% CI |      p
-----------------------------------------
n01     |      0.01 | 0.00, 0.02 | 0.037 
n02     |      0.05 | 0.04, 0.06 | < .001
n03     |      0.09 | 0.08, 0.10 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |       95% CI |      p
------------------------------------------
n01-n02 |    -0.04 | -0.05, -0.03 | < .001
n01-n03 |    -0.07 | -0.08, -0.07 | < .001
n02-n03 |    -0.03 | -0.04, -0.03 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FPR

KSS | Predicted |     95% CI
----------------------------
  1 |      0.03 | 0.02, 0.05
  2 |      0.04 | 0.02, 0.05
  3 |      0.04 | 0.03, 0.05
  4 |      0.05 | 0.04, 0.06
  5 |      0.05 | 0.04, 0.06
  6 |      0.06 | 0.04, 0.07
  7 |      0.06 | 0.05, 0.08
  8 |      0.07 | 0.05, 0.09
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope    |      95% CI |     p
------------------------------
4.58e-03 |  0.00, 0.01 | 0.076
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope    |      95% CI |     p
------------------------------
4.58e-03 |  0.00, 0.01 | 0.076

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FPR

Gender | Predicted |     95% CI
-------------------------------
f      |      0.05 | 0.04, 0.07
m      |      0.04 | 0.03, 0.06
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |     95% CI |      p
----------------------------------------
f      |      0.05 | 0.04, 0.07 | < .001
m      |      0.04 | 0.03, 0.06 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |      95% CI |     p
---------------------------------------
f-m    |     0.01 | -0.01, 0.03 | 0.204

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Check Model fit.FNR

Code
model <- "fit.FNR"
fbase <- get_model_info(model, ofd0)
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +  
    (1 | Sub)
   Data: df2
Control: control

REML criterion at convergence: -704.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5049 -0.6101 -0.0791  0.4992  3.6616 

Random effects:
 Groups   Name        Variance Std.Dev.
 Sub      (Intercept) 0.006864 0.08285 
 Residual             0.022360 0.14953 
Number of obs: 891, groups:  Sub, 51

Fixed effects:
                    Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)         0.021180   0.031344 200.304268   0.676  0.50000    
SesEvening         -0.001712   0.010144 846.426854  -0.169  0.86597    
ChronoEvening       0.013169   0.026008  51.086968   0.506  0.61478    
Run02              -0.034231   0.012271 833.660412  -2.790  0.00540 ** 
Run03              -0.032548   0.012271 833.660412  -2.652  0.00814 ** 
ConditIncongruent   0.005493   0.016743 843.615673   0.328  0.74293    
NumBackn02          0.101010   0.012271 833.660412   8.232 7.09e-16 ***
NumBackn03          0.280022   0.012271 833.660412  22.820  < 2e-16 ***
KSS                 0.010352   0.006924 765.128571   1.495  0.13533    
Genderm            -0.055245   0.025690  48.327741  -2.150  0.03655 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# R2 for Mixed Models

  Conditional R2: 0.491
     Marginal R2: 0.335
--------------------------------------------------------------------- 
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.235
  Unadjusted ICC: 0.156
--------------------------------------------------------------------- 
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.235
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FNR

Ses     | Predicted |     95% CI
--------------------------------
Morning |      0.15 | 0.13, 0.18
Evening |      0.15 | 0.13, 0.18
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.15 | 0.13, 0.18 | < .001
Morning |      0.15 | 0.13, 0.18 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             |  Contrast |      95% CI |     p
-------------------------------------------------
Evening-Morning | -1.71e-03 | -0.02, 0.02 | 0.866

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FNR

Chrono  | Predicted |     95% CI
--------------------------------
Morning |      0.15 | 0.11, 0.18
Evening |      0.16 | 0.12, 0.20
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.16 | 0.12, 0.20 | < .001
Morning |      0.15 | 0.11, 0.18 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning |     0.01 | -0.04, 0.06 | 0.613

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FNR

Run | Predicted |     95% CI
----------------------------
01  |      0.18 | 0.15, 0.20
02  |      0.14 | 0.11, 0.17
03  |      0.14 | 0.11, 0.17
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |     95% CI |      p
-------------------------------------
01  |      0.18 | 0.15, 0.20 | < .001
02  |      0.14 | 0.11, 0.17 | < .001
03  |      0.14 | 0.11, 0.17 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   |  Contrast |      95% CI |     p
---------------------------------------
01-02 |      0.03 |  0.01, 0.06 | 0.012
01-03 |      0.03 |  0.01, 0.06 | 0.012
02-03 | -1.68e-03 | -0.03, 0.02 | 0.891

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FNR

Condit      | Predicted |     95% CI
------------------------------------
Congruent   |      0.15 | 0.12, 0.18
Incongruent |      0.16 | 0.13, 0.19
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |     95% CI |      p
---------------------------------------------
Congruent   |      0.15 | 0.12, 0.18 | < .001
Incongruent |      0.16 | 0.13, 0.19 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                |  Contrast |      95% CI |     p
-------------------------------------------------------
Congruent-Incongruent | -5.49e-03 | -0.04, 0.03 | 0.743

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FNR

NumBack | Predicted |      95% CI
---------------------------------
n01     |      0.03 |  0.00, 0.05
n02     |      0.13 |  0.10, 0.16
n03     |      0.31 |  0.28, 0.33
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |      95% CI |      p
------------------------------------------
n01     |      0.03 |  0.00, 0.05 | 0.069 
n02     |      0.13 |  0.10, 0.16 | < .001
n03     |      0.31 |  0.28, 0.33 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |       95% CI |      p
------------------------------------------
n01-n02 |    -0.10 | -0.13, -0.08 | < .001
n01-n03 |    -0.28 | -0.30, -0.26 | < .001
n02-n03 |    -0.18 | -0.20, -0.15 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FNR

KSS | Predicted |     95% CI
----------------------------
  1 |      0.12 | 0.07, 0.17
  2 |      0.13 | 0.09, 0.17
  3 |      0.14 | 0.11, 0.17
  4 |      0.15 | 0.13, 0.18
  5 |      0.16 | 0.13, 0.19
  6 |      0.17 | 0.14, 0.21
  7 |      0.18 | 0.14, 0.23
  8 |      0.19 | 0.14, 0.25
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope |      95% CI |     p
---------------------------
0.01  |  0.00, 0.02 | 0.135
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope |      95% CI |     p
---------------------------
0.01  |  0.00, 0.02 | 0.135

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FNR

Gender | Predicted |     95% CI
-------------------------------
f      |      0.18 | 0.14, 0.21
m      |      0.12 | 0.09, 0.16
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |     95% CI |      p
----------------------------------------
f      |      0.18 | 0.14, 0.21 | < .001
m      |      0.12 | 0.09, 0.16 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |     95% CI |     p
--------------------------------------
f-m    |     0.06 | 0.00, 0.11 | 0.032

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Check Model fit.FDR

Code
model <- "fit.FDR"
fbase <- get_model_info(model, ofd0)
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +  
    (1 | Sub)
   Data: df2
Control: control

REML criterion at convergence: -903.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.8078 -0.6102 -0.0325  0.4965  4.6505 

Random effects:
 Groups   Name        Variance Std.Dev.
 Sub      (Intercept) 0.005836 0.0764  
 Residual             0.017732 0.1332  
Number of obs: 889, groups:  Sub, 51

Fixed effects:
                    Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)        4.386e-04  2.831e-02  1.914e+02   0.015   0.9877    
SesEvening         4.639e-03  9.048e-03  8.436e+02   0.513   0.6083    
ChronoEvening      1.130e-02  2.384e-02  5.034e+01   0.474   0.6375    
Run02             -1.955e-02  1.094e-02  8.310e+02  -1.787   0.0742 .  
Run03             -2.229e-02  1.095e-02  8.310e+02  -2.036   0.0420 *  
ConditIncongruent -1.412e-03  1.498e-02  8.483e+02  -0.094   0.9250    
NumBackn02         1.134e-01  1.093e-02  8.310e+02  10.381   <2e-16 ***
NumBackn03         2.321e-01  1.095e-02  8.311e+02  21.201   <2e-16 ***
KSS                1.078e-02  6.196e-03  7.782e+02   1.739   0.0824 .  
Genderm           -3.718e-02  2.356e-02  4.768e+01  -1.578   0.1212    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# R2 for Mixed Models

  Conditional R2: 0.467
     Marginal R2: 0.291
--------------------------------------------------------------------- 
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.248
  Unadjusted ICC: 0.176
--------------------------------------------------------------------- 
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.248
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FDR

Ses     | Predicted |     95% CI
--------------------------------
Morning |      0.14 | 0.11, 0.16
Evening |      0.14 | 0.12, 0.16
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.14 | 0.12, 0.16 | < .001
Morning |      0.14 | 0.11, 0.16 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning | 4.64e-03 | -0.01, 0.02 | 0.608

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FDR

Chrono  | Predicted |     95% CI
--------------------------------
Morning |      0.13 | 0.10, 0.16
Evening |      0.14 | 0.11, 0.18
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.14 | 0.11, 0.18 | < .001
Morning |      0.13 | 0.10, 0.16 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning |     0.01 | -0.04, 0.06 | 0.636

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FDR

Run | Predicted |     95% CI
----------------------------
01  |      0.15 | 0.13, 0.18
02  |      0.13 | 0.11, 0.16
03  |      0.13 | 0.10, 0.16
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |     95% CI |      p
-------------------------------------
01  |      0.15 | 0.13, 0.18 | < .001
02  |      0.13 | 0.11, 0.16 | < .001
03  |      0.13 | 0.10, 0.16 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   | Contrast |      95% CI |     p
--------------------------------------
01-02 |     0.02 |  0.00, 0.04 | 0.111
01-03 |     0.02 |  0.00, 0.04 | 0.111
02-03 | 2.74e-03 | -0.02, 0.02 | 0.802

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FDR

Condit      | Predicted |     95% CI
------------------------------------
Congruent   |      0.14 | 0.11, 0.17
Incongruent |      0.14 | 0.11, 0.16
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |     95% CI |      p
---------------------------------------------
Congruent   |      0.14 | 0.11, 0.17 | < .001
Incongruent |      0.14 | 0.11, 0.16 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                | Contrast |      95% CI |     p
------------------------------------------------------
Congruent-Incongruent | 1.41e-03 | -0.03, 0.03 | 0.925

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FDR

NumBack | Predicted |      95% CI
---------------------------------
n01     |      0.02 |  0.00, 0.05
n02     |      0.14 |  0.11, 0.16
n03     |      0.26 |  0.23, 0.28
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |      95% CI |      p
------------------------------------------
n01     |      0.02 |  0.00, 0.05 | 0.082 
n02     |      0.14 |  0.11, 0.16 | < .001
n03     |      0.26 |  0.23, 0.28 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |       95% CI |      p
------------------------------------------
n01-n02 |    -0.11 | -0.13, -0.09 | < .001
n01-n03 |    -0.23 | -0.25, -0.21 | < .001
n02-n03 |    -0.12 | -0.14, -0.10 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FDR

KSS | Predicted |     95% CI
----------------------------
  1 |      0.10 | 0.06, 0.15
  2 |      0.11 | 0.08, 0.15
  3 |      0.12 | 0.10, 0.15
  4 |      0.14 | 0.11, 0.16
  5 |      0.15 | 0.12, 0.17
  6 |      0.16 | 0.13, 0.19
  7 |      0.17 | 0.13, 0.21
  8 |      0.18 | 0.13, 0.23
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope |      95% CI |     p
---------------------------
0.01  |  0.00, 0.02 | 0.082
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope |      95% CI |     p
---------------------------
0.01  |  0.00, 0.02 | 0.082

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FDR

Gender | Predicted |     95% CI
-------------------------------
f      |      0.15 | 0.12, 0.19
m      |      0.12 | 0.08, 0.15
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |     95% CI |      p
----------------------------------------
f      |      0.15 | 0.12, 0.19 | < .001
m      |      0.12 | 0.08, 0.15 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |      95% CI |     p
---------------------------------------
f-m    |     0.04 | -0.01, 0.08 | 0.115

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Check Model fit.NPV

Code
model <- "fit.NPV"
fbase <- get_model_info(model, ofd0)
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +  
    (1 | Sub)
   Data: df2
Control: control

REML criterion at convergence: -2775.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.3747 -0.5254  0.0772  0.6310  2.6182 

Random effects:
 Groups   Name        Variance  Std.Dev.
 Sub      (Intercept) 0.0006779 0.02604 
 Residual             0.0021282 0.04613 
Number of obs: 891, groups:  Sub, 51

Fixed effects:
                    Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)        9.928e-01  9.742e-03  1.966e+02 101.907  < 2e-16 ***
SesEvening        -1.615e-04  3.130e-03  8.461e+02  -0.052  0.95885    
ChronoEvening     -4.273e-03  8.146e-03  5.099e+01  -0.525  0.60219    
Run02              1.030e-02  3.786e-03  8.336e+02   2.721  0.00664 ** 
Run03              1.044e-02  3.786e-03  8.336e+02   2.757  0.00595 ** 
ConditIncongruent -1.883e-03  5.174e-03  8.472e+02  -0.364  0.71592    
NumBackn02        -3.229e-02  3.786e-03  8.336e+02  -8.530  < 2e-16 ***
NumBackn03        -8.851e-02  3.786e-03  8.336e+02 -23.381  < 2e-16 ***
KSS               -3.139e-03  2.141e-03  7.734e+02  -1.466  0.14299    
Genderm            1.773e-02  8.050e-03  4.829e+01   2.202  0.03249 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# R2 for Mixed Models

  Conditional R2: 0.502
     Marginal R2: 0.344
--------------------------------------------------------------------- 
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.242
  Unadjusted ICC: 0.159
--------------------------------------------------------------------- 
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.242
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of NPV

Ses     | Predicted |     95% CI
--------------------------------
Morning |      0.95 | 0.94, 0.96
Evening |      0.95 | 0.94, 0.96
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.95 | 0.94, 0.96 | < .001
Morning |      0.95 | 0.94, 0.96 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             |  Contrast |      95% CI |     p
-------------------------------------------------
Evening-Morning | -1.62e-04 | -0.01, 0.01 | 0.959

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of NPV

Chrono  | Predicted |     95% CI
--------------------------------
Morning |      0.95 | 0.94, 0.96
Evening |      0.95 | 0.94, 0.96
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.95 | 0.94, 0.96 | < .001
Morning |      0.95 | 0.94, 0.96 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          |  Contrast |      95% CI |     p
-------------------------------------------------
Evening-Morning | -4.27e-03 | -0.02, 0.01 | 0.600

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of NPV

Run | Predicted |     95% CI
----------------------------
01  |      0.94 | 0.94, 0.95
02  |      0.95 | 0.95, 0.96
03  |      0.95 | 0.95, 0.96
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |     95% CI |      p
-------------------------------------
01  |      0.94 | 0.94, 0.95 | < .001
02  |      0.95 | 0.95, 0.96 | < .001
03  |      0.95 | 0.95, 0.96 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   |  Contrast |       95% CI |     p
----------------------------------------
01-02 |     -0.01 | -0.02,  0.00 | 0.010
01-03 |     -0.01 | -0.02,  0.00 | 0.010
02-03 | -1.37e-04 | -0.01,  0.01 | 0.971

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of NPV

Condit      | Predicted |     95% CI
------------------------------------
Congruent   |      0.95 | 0.94, 0.96
Incongruent |      0.95 | 0.94, 0.96
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |     95% CI |      p
---------------------------------------------
Congruent   |      0.95 | 0.94, 0.96 | < .001
Incongruent |      0.95 | 0.94, 0.96 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                | Contrast |      95% CI |     p
------------------------------------------------------
Congruent-Incongruent | 1.88e-03 | -0.01, 0.01 | 0.716

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of NPV

NumBack | Predicted |     95% CI
--------------------------------
n01     |      0.99 | 0.98, 1.00
n02     |      0.96 | 0.95, 0.97
n03     |      0.90 | 0.89, 0.91
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |     95% CI |      p
-----------------------------------------
n01     |      0.99 | 0.98, 1.00 | < .001
n02     |      0.96 | 0.95, 0.97 | < .001
n03     |      0.90 | 0.89, 0.91 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |     95% CI |      p
----------------------------------------
n01-n02 |     0.03 | 0.02, 0.04 | < .001
n01-n03 |     0.09 | 0.08, 0.10 | < .001
n02-n03 |     0.06 | 0.05, 0.06 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of NPV

KSS | Predicted |     95% CI
----------------------------
  1 |      0.96 | 0.95, 0.98
  2 |      0.96 | 0.95, 0.97
  3 |      0.95 | 0.95, 0.96
  4 |      0.95 | 0.94, 0.96
  5 |      0.95 | 0.94, 0.96
  6 |      0.95 | 0.93, 0.96
  7 |      0.94 | 0.93, 0.96
  8 |      0.94 | 0.92, 0.96
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope     |      95% CI |     p
-------------------------------
-3.14e-03 | -0.01, 0.00 | 0.143
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope     |      95% CI |     p
-------------------------------
-3.14e-03 | -0.01, 0.00 | 0.143

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of NPV

Gender | Predicted |     95% CI
-------------------------------
f      |      0.94 | 0.93, 0.95
m      |      0.96 | 0.95, 0.97
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |     95% CI |      p
----------------------------------------
f      |      0.94 | 0.93, 0.95 | < .001
m      |      0.96 | 0.95, 0.97 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |       95% CI |     p
----------------------------------------
f-m    |    -0.02 | -0.03,  0.00 | 0.028

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Check Model fit.FOR

Code
model <- "fit.FOR"
fbase <- get_model_info(model, ofd0)
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +  
    (1 | Sub)
   Data: df2
Control: control

REML criterion at convergence: -2775.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6182 -0.6310 -0.0772  0.5254  3.3747 

Random effects:
 Groups   Name        Variance  Std.Dev.
 Sub      (Intercept) 0.0006779 0.02604 
 Residual             0.0021282 0.04613 
Number of obs: 891, groups:  Sub, 51

Fixed effects:
                    Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)        7.191e-03  9.742e-03  1.966e+02   0.738  0.46131    
SesEvening         1.615e-04  3.130e-03  8.461e+02   0.052  0.95885    
ChronoEvening      4.273e-03  8.146e-03  5.099e+01   0.525  0.60219    
Run02             -1.030e-02  3.786e-03  8.336e+02  -2.721  0.00664 ** 
Run03             -1.044e-02  3.786e-03  8.336e+02  -2.757  0.00595 ** 
ConditIncongruent  1.883e-03  5.174e-03  8.472e+02   0.364  0.71592    
NumBackn02         3.229e-02  3.786e-03  8.336e+02   8.530  < 2e-16 ***
NumBackn03         8.851e-02  3.786e-03  8.336e+02  23.381  < 2e-16 ***
KSS                3.139e-03  2.141e-03  7.734e+02   1.466  0.14299    
Genderm           -1.773e-02  8.050e-03  4.829e+01  -2.202  0.03249 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------- 
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# R2 for Mixed Models

  Conditional R2: 0.502
     Marginal R2: 0.344
--------------------------------------------------------------------- 
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# Intraclass Correlation Coefficient

    Adjusted ICC: 0.242
  Unadjusted ICC: 0.159
--------------------------------------------------------------------- 
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
# ICC by Group

Group |   ICC
-------------
Sub   | 0.242
--------------------------------------------------------------------- 

Effect of Ses

Code
extra <- "1001"
terms <- c("Ses")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FOR

Ses     | Predicted |     95% CI
--------------------------------
Morning |      0.05 | 0.04, 0.06
Evening |      0.05 | 0.04, 0.06
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Ses     | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.05 | 0.04, 0.06 | < .001
Morning |      0.05 | 0.04, 0.06 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Ses             | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning | 1.62e-04 | -0.01, 0.01 | 0.959

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Chrono

Code
extra <- "1002"
terms <- c("Chrono")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FOR

Chrono  | Predicted |     95% CI
--------------------------------
Morning |      0.05 | 0.04, 0.06
Evening |      0.05 | 0.04, 0.06
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Chrono  | Predicted |     95% CI |      p
-----------------------------------------
Evening |      0.05 | 0.04, 0.06 | < .001
Morning |      0.05 | 0.04, 0.06 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Chrono          | Contrast |      95% CI |     p
------------------------------------------------
Evening-Morning | 4.27e-03 | -0.01, 0.02 | 0.600

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Run

Code
extra <- "1003"
terms <- c("Run")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FOR

Run | Predicted |     95% CI
----------------------------
01  |      0.06 | 0.05, 0.06
02  |      0.05 | 0.04, 0.05
03  |      0.05 | 0.04, 0.05
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Run | Predicted |     95% CI |      p
-------------------------------------
01  |      0.06 | 0.05, 0.06 | < .001
02  |      0.05 | 0.04, 0.05 | < .001
03  |      0.05 | 0.04, 0.05 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Run   | Contrast |      95% CI |     p
--------------------------------------
01-02 |     0.01 |  0.00, 0.02 | 0.010
01-03 |     0.01 |  0.00, 0.02 | 0.010
02-03 | 1.37e-04 | -0.01, 0.01 | 0.971

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Condit

Code
extra <- "1004"
terms <- c("Condit")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FOR

Condit      | Predicted |     95% CI
------------------------------------
Congruent   |      0.05 | 0.04, 0.06
Incongruent |      0.05 | 0.04, 0.06
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Condit      | Predicted |     95% CI |      p
---------------------------------------------
Congruent   |      0.05 | 0.04, 0.06 | < .001
Incongruent |      0.05 | 0.04, 0.06 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Condit                |  Contrast |      95% CI |     p
-------------------------------------------------------
Congruent-Incongruent | -1.88e-03 | -0.01, 0.01 | 0.716

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of NumBack

Code
extra <- "1005"
terms <- c("NumBack")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FOR

NumBack | Predicted |      95% CI
---------------------------------
n01     |      0.01 |  0.00, 0.02
n02     |      0.04 |  0.03, 0.05
n03     |      0.10 |  0.09, 0.11
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
NumBack | Predicted |      95% CI |      p
------------------------------------------
n01     |  8.76e-03 |  0.00, 0.02 | 0.054 
n02     |      0.04 |  0.03, 0.05 | < .001
n03     |      0.10 |  0.09, 0.11 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

NumBack | Contrast |       95% CI |      p
------------------------------------------
n01-n02 |    -0.03 | -0.04, -0.02 | < .001
n01-n03 |    -0.09 | -0.10, -0.08 | < .001
n02-n03 |    -0.06 | -0.06, -0.05 | < .001

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of KSS

Code
extra <- "1005"
terms <- c("KSS")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FOR

KSS | Predicted |     95% CI
----------------------------
  1 |      0.04 | 0.02, 0.05
  2 |      0.04 | 0.03, 0.05
  3 |      0.05 | 0.04, 0.05
  4 |      0.05 | 0.04, 0.06
  5 |      0.05 | 0.04, 0.06
  6 |      0.05 | 0.04, 0.07
  7 |      0.06 | 0.04, 0.07
  8 |      0.06 | 0.04, 0.08
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
# (Average) Linear trend for KSS

Slope    |      95% CI |     p
------------------------------
3.14e-03 |  0.00, 0.01 | 0.143
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# (Average) Linear trend for KSS

Slope    |      95% CI |     p
------------------------------
3.14e-03 |  0.00, 0.01 | 0.143

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Effect of Gender

Code
extra <- "1006"
terms <- c("Gender")

suppressWarnings(rm(list = ls(pattern = "^ggeff")))
ggeff <- get_eff_null(model, terms, extra, ofd0)
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + 
    (1 | Sub)
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$pred0, n = Inf)
# Average predicted values of FOR

Gender | Predicted |     95% CI
-------------------------------
f      |      0.06 | 0.05, 0.07
m      |      0.04 | 0.03, 0.05
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test0, n = Inf)
Gender | Predicted |     95% CI |      p
----------------------------------------
f      |      0.06 | 0.05, 0.07 | < .001
m      |      0.04 | 0.03, 0.05 | < .001
Code
cat0(sep0)
===================================================================== 
Code
print(ggeff$test2, n = Inf)
# Pairwise comparisons

Gender | Contrast |     95% CI |     p
--------------------------------------
f-m    |     0.02 | 0.00, 0.03 | 0.028

Plot Effect

Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA

ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)

gg88

Code
ls()
 [1] "cat0"           "control"        "custom_palette" "df0"           
 [5] "df2"            "extra"          "fbase"          "fit.Accur"     
 [9] "fit.FDR"        "fit.FN"         "fit.FNR"        "fit.FOR"       
[13] "fit.FP"         "fit.FPR"        "fit.NPV"        "fit.Preci"     
[17] "fit.RT"         "fit.Sensi"      "fit.Speci"      "fit.TN"        
[21] "fit.TP"         "get_eff_null"   "get_model_info" "gg88"          
[25] "ggeff"          "ifd0"           "ifn0"           "line0h"        
[29] "model"          "ofd0"           "pp"             "REML"          
[33] "sep0"           "sep1"           "sep2"           "terms"